Oxford-Matplotlib-Theme

Oxford Matplotlib Theme

Official Oxford University colors and styling for Matplotlib visualizations.

Python 3.8+ License: MIT

Create publication-quality figures with official Oxford University branding for academic papers, presentations, and reports.

Author: Sami Adnan Nuffield Department of Primary Care Health Sciences University of Oxford

Features

Official Oxford Branding

13 Curated Color Palettes

7 Pre-configured Presets

Journal-Ready Export

Zero Browser Dependencies

Installation

# Clone the repository
git clone https://github.com/sami5001/oxford-matplotlib-theme.git
cd oxford-matplotlib-theme

# Install in editable mode
pip install -e .

# Or install with all dependencies
pip install -e ".[all]"

Dependencies

Core (required):

Examples (optional):

Quick Start

Basic Usage

import matplotlib.pyplot as plt
from oxford_matplotlib_theme import apply_oxford_theme

# Apply Oxford theme globally
apply_oxford_theme()

# Create your plot
plt.plot([1, 2, 3], [1, 4, 9])
plt.title('My Oxford Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Using the Convenience Function

from oxford_matplotlib_theme import oxford_figure

# Create pre-styled figure
fig, ax = oxford_figure()
ax.plot([1, 2, 3], [1, 4, 9])
ax.set_title('My Oxford Plot')
plt.show()

Using Presets

from oxford_matplotlib_theme import apply_preset

# Larger fonts for presentations
apply_preset('presentation')

# Create plots - automatically uses larger fonts
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])
plt.show()

Custom Colors

from oxford_matplotlib_theme import get_palette, OXFORD_COLORS

# Use specific Oxford colors
plt.plot(x, y, color=OXFORD_COLORS['coral'])

# Get a color palette
colors = get_palette('vibrant', n_colors=5)
for i, color in enumerate(colors):
    plt.plot(x, data[i], color=color, label=f'Series {i+1}')

Export for Journal Submission

from oxford_matplotlib_theme import save_oxford_figure, get_journal_preset

# Get Nature journal preset
preset = get_journal_preset('nature')

# Create figure with journal size
fig, ax = oxford_figure(figsize=preset['figsize'])
ax.plot(x, y)
ax.set_title('My Research')

# Save with journal settings
save_oxford_figure(fig, 'figure1', format=preset['format'], dpi=preset['dpi'])
# Creates: figure1.svg at 300 DPI

Color Palettes

All palettes use official Oxford University brand colors:

Palette Colors Use Case
PRIMARY 10 General purpose, most versatile
PROFESSIONAL 6 Business/academic presentations
VIBRANT 7 Eye-catching visualizations
PASTEL 7 Softer, subtle visualizations
DIVERGING 5 Data with meaningful center point
SEQUENTIAL_BLUE 5 Continuous/ordered data
HEALTH 5 Medical/PHC department specific
TRADITIONAL 6 Heritage and stability
CONTEMPORARY 6 Modern and clean
CELEBRATORY 6 Festive and bright
CORPORATE 6 Professional and formal
INNOVATIVE 6 Tech-focused and forward-looking
PHC_THESIS 27 Comprehensive thesis palette

Access palettes:

from oxford_matplotlib_theme import get_palette, ColorPalettes

# Get palette as list
colors = get_palette('vibrant')

# Access palette class
all_professional = ColorPalettes.PROFESSIONAL

Available Presets

Preset Description Font Scale
default Standard for academic papers 1.0
presentation Larger fonts for slides 1.4
poster Extra large for conference posters 1.8
colorblind Accessible color palette 1.0
minimal Clean with grid background 1.0
notebook Optimized for Jupyter 1.1
print High contrast for printing 1.0 
from oxford_matplotlib_theme import apply_preset, list_presets

# See all presets
list_presets()

# Use a preset
apply_preset('presentation')

Journal Export Presets

Built-in settings for major journals:

Journal Figure Size (inches) DPI Format
Nature (single) 3.5 × 2.5 300 SVG
Nature (double) 7.0 × 5.0 300 SVG
PLOS 6.83 × 5.0 300 TIFF
BMJ 3.27 × 2.5 300 EPS
Lancet 3.27 × 2.5 300 TIFF 
from oxford_matplotlib_theme import get_journal_preset

preset = get_journal_preset('nature')
# Returns: {'figsize': (3.5, 2.5), 'dpi': 300, 'format': 'svg'}

Examples

Line Plot with Multiple Series

import numpy as np
from oxford_matplotlib_theme import apply_oxford_theme

apply_oxford_theme()

x = np.linspace(0, 10, 100)
fig, ax = plt.subplots(figsize=(10, 6))

ax.plot(x, np.sin(x), label='sin(x)', linewidth=2)
ax.plot(x, np.cos(x), label='cos(x)', linewidth=2)

ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.set_title('Trigonometric Functions')
ax.legend()
ax.grid(True, alpha=0.3)
plt.show()

Bar Chart with Oxford Colors

from oxford_matplotlib_theme import get_palette

categories = ['A', 'B', 'C', 'D']
values = [23, 45, 56, 78]
colors = get_palette('professional', n_colors=4)

fig, ax = plt.subplots()
ax.bar(categories, values, color=colors[0])
ax.set_ylabel('Values')
ax.set_title('Category Comparison')
ax.grid(True, axis='y', alpha=0.3)
plt.show()

ROC Curve for Clinical ML

from sklearn.metrics import roc_curve, auc
from oxford_matplotlib_theme import apply_oxford_theme, get_palette

apply_oxford_theme()
colors = get_palette('primary', n_colors=3)

# Assuming you have y_true and y_pred
fpr, tpr, _ = roc_curve(y_true, y_pred)
roc_auc = auc(fpr, tpr)

fig, ax = plt.subplots(figsize=(8, 8))
ax.plot(fpr, tpr, color=colors[0], linewidth=2.5,
       label=f'ROC curve (AUC = {roc_auc:.3f})')
ax.plot([0, 1], [0, 1], 'k--', linewidth=1.5, alpha=0.5)

ax.set_xlabel('False Positive Rate')
ax.set_ylabel('True Positive Rate')
ax.set_title('ROC Curve - Clinical Prediction Model')
ax.legend(loc='lower right')
ax.grid(True, alpha=0.3)
plt.show()

More Examples

See the examples/ directory for complete runnable examples:

Advanced Usage

Custom Color Cycles

from oxford_matplotlib_theme import apply_oxford_theme

# Use specific colors in custom order
custom_colors = ['coral', 'aqua', 'oxford_blue', 'vivid_green']
apply_oxford_theme(color_cycle=custom_colors)

Font Scaling

# Make all fonts 50% larger
apply_oxford_theme(font_scale=1.5)

# For presentations
apply_oxford_theme(font_scale=1.4)

# For posters
apply_oxford_theme(font_scale=1.8)

Adding Branding

from oxford_matplotlib_theme import add_oxford_branding

fig, ax = oxford_figure()
ax.plot([1, 2, 3], [1, 4, 9])

# Add watermark
add_oxford_branding(
    fig,
    add_watermark=True,
    watermark_text='University of Oxford',
    position='bottom_right',
    opacity=0.5
)
plt.show()

Migration from oxford-plotly-theme

If you’re migrating from the Plotly version:

Plotly Matplotlib
create_oxford_figure() oxford_figure() returns (fig, ax)
fig.add_trace(go.Scatter(...)) ax.plot(...)
get_color_palette() get_palette()
fig.write_image() (needs kaleido) fig.savefig() (native)
Manual sizing get_journal_preset()

Key benefits of matplotlib version:

See examples/migration_comparison.py and docs/MIGRATION_GUIDE.md for detailed migration instructions.

Documentation

Contributing

This theme is maintained for internal use at the University of Oxford. For bug reports or feature requests, please open an issue on GitHub.

License

MIT License - Copyright (c) 2025 Sami Adnan

Based on Oxford University Brand Guidelines

Acknowledgments

Contact

Sami Adnan Nuffield Department of Primary Care Health Sciences University of Oxford

For questions about Oxford brand guidelines, contact the Oxford Communications Office.

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